• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3856-3872
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• 2018

HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS system, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment and stores it in the playout buffer. The rate adaptation algorithm employed in HAS clients dynamically determines the video bitrate depending on the time-varying bandwidth. Many studies have shown that an efficient rate adaptation algorithm is critical to ensuring quality-of-experience in HAS systems. However, existing algorithms have problems estimating the network bandwidth because bandwidth estimation is performed on the client-side application stack. Without the help of transport layer protocols, it is difficult to achieve accurate bandwidth estimation due to the inherent segment-based transmission of the HAS. In this paper, we propose an alternative approach that utilizes the playout buffer occupancy rather than using bandwidth estimates obtained from the application layer. We start with a queueing analysis of the playout buffer. Then, we present a buffer-aware rate adaptation algorithm that is solely based on the mean buffer occupancy. Our simulation results show that compared to conventional algorithms, the proposed algorithm achieves very smooth video quality while delivering a similar average video bitrate.

• Computers and Concrete
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• v.31 no.2
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• pp.85-95
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• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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• pp.74-82
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• 2004

In this paper, a disturbance feedforward compensator design technique is proposed for an active magnetic bearing system subject to base motion for attenuating disturbance responses. In the consideration of the requirements on the model accuracy in the model based compensator designs, an experimental feedforward compensator design based on adaptive estimation by means of the Multiple Filtered-x least mean square(MFXLMS) algorithm is proposed. The performance and the effectiveness of the proposed technique will be presented in the succeeding paper in which the proposed technique is applied to a 2-DOF active magnetic bearing system subject to base motion.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2477-2479
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• 2005

A robust bending frequency tracker is proposed to design the adaptive notch filter which removes the time-varying missile structural modes from the sensor measurements. To do this, the state-space form of a bending frequency model is derived under the assumption that the bending signal could be described as the lightly damped sinusoid. Since the resultant bending frequency model contains the parametric uncertainties in the measurement matrix, the design problem of bending frequency tracker is tackled by applying the robust Kalman filter to the model. This technique could be easily expanded to the multiple frequencies case because it newly illuminates the bending frequency tracking problem in view of general state estimation.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.87-91
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• 2002

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the state of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, a DNA coding-based interacting multiple model (DNA coding-based IMM) method is proposed. The proposed method can overcome the mathematical limits of conventional methods by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive IMM algorithm and the GA-based IMM method in computer simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1603-1623
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• 2020

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A²CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A²CL algorithm's performance finally. Results proved that the presented A²CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4929-4947
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• 2017

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.385-390
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• 2009

This paper presents a novel active noise control (ANC) system using least mean square (LMS) algorithm and neural network approach for decreasing acoustic noise signals inside high-speed trains. We construct a LMS framework as a nominal ANC system and additionally design an artificial single-layered perceptron model as an auxiliary ANC which is aimed to reduce real-time residuary noise due to its nonstationary and uncertain nature. Parameter vector of the hybrid ANC is determined through online estimation to realize an adaptive ANC configuration by means of the steepest descent algorithm. We achieve simulation experiment to demonstrate the proposed ANC system employing realistic acoustic noise signals measured in Korea Train eXpress (KTX).

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2011-2013
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• 2001

In this paper the MMAE(Multiple Model Adaptive Estimation) algorithm using the MGEKF(Modified Gain Extended Kalman Filter) of which modes are set to be measurement biases is proposed to enhance the performance of target tracking with bearing only measurements. The state are composed of relative position, relative velocity and taregt acceleration. The mode probability is calculated from the bearing only measurements from the HMS(Hull-Mounted Sonar). The proposed algorithm is tested in a series of computer simulation runs.